Empowering Mechanism, Practical Dilemmas and Optimization Strategies of Digital-Intelligent Technology in Government Low-Carbon Governance
DOI:
https://doi.org/10.54097/8b4xx927Keywords:
Digital-intelligent Technology, Government Low-carbon Governance, Empowering Mechanism, Dual Carbon StrategyAbstract
Digital-intelligent technology provides crucial technical support for addressing government low-carbon governance dilemmas and improving regional low-carbon governance capacity. This paper systematically explores the empowering mechanism and practical dilemmas of digital-intelligent technology in low-carbon governance. The results indicate that digital-intelligent technology empowers government low-carbon governance in three core aspects: decision-making, process and governance mode. It transforms empirical and lagging governance decision-making into scientific prediction, fragmented decentralized governance into multi-subject collaborative co-governance, and extensive administrative management into refined precise governance. In practice, digital low-carbon governance is restricted by three prominent dilemmas: cognitive bias of technological omnipotence, misalignment between technology and institutional systems, and insufficient comprehensive capacity of diverse governance subjects. This paper proposes targeted optimization strategies to correct cognitive deviations, promote technology-institution collaborative adaptation and make up for multi-subject capacity deficiencies, aiming to provide theoretical references and practical guidance for improving government low-carbon governance efficiency.
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References
[1] Climate Change Center of China Meteorological Administration. (2025). China Climate Change Blue Book (2025). Science Press.
[2] IEA. (2025, March 24). Global Energy Review 2025 [Report]. https://www.iea.org/reports/global-energy-review-2025.
[3] Xi, J. P. (2022). Secure a decisive victory in building a modern socialist country in all respects and strive for the great success of the Chinese nation’s great rejuvenation through Chinese-style modernization—Report to the 20th National Congress of the Communist Party of China. People’s Publishing House.
[4] Peng, B., & Liu, X. (2022). Solutions to coordination dilemma in primary-level governance: The Tiao/Kuai unified approaches of digital platform. Theory and Reform, (5), 42–56.
[5] Yu, M. J. (2022). Compound fragmentation: Why is environmental governance difficult to be refined?--Analysis based on the perspective of holistic governance. Chinese Public Administration, (9), 89–96.
[6] He, K. W., Zhang, W. B., & Zhang, N. (2025). Digital empowerment and China’s energy conservation and carbon reduction efficiency: Mechanism and effect. Journal of China University of Geosciences (Social Sciences Edition), 25(1), 82–99.
[7] Han, P., He, T., Feng, C., et al. (2024). Research on whether artificial intelligence affects industrial carbon emission intensity based on the perspective of industrial structure and government intervention. Sustainability, 16(21), 9368. https://doi.org/10.3390/su16219368.
[8] Li, J., & Zhao, J. (2023). Digital empowerment and regulatory efficiency improvement in environmental governance: From the perspective of labor substitution. China Population, Resources and Environment, 33(8), 130–137.
[9] Xie, W. D. (2025). How does government digital transformation improve local environmental governance efficiency?—A multi-dimensional investigation of institution, technology and behavior. E-Government, (10), 119–132.
[10] Jiang, B., Cao, T. X., & Kang, W. (2022). Research on the organizational structure change of grassroots government driven by digital government--Based on the case of Nanhai District Government of Foshan City. Journal of Public Management, 19(2), 72–81+169.
[11] Huang, X. H., & Wang, L. C. (2025). Algorithmic governance in public administration: Rise, risks, and regulation. Journal of Beijing Administrative College, (1), 64–72.
[12] Li, T., Li, X. P., Shangguan, F. Q., Zhang, W., Zhou, J. C., Ni, B., Duan, Z. W., & Teng, G. X. (2024). Advancing digital carbon management in China's steel industry. Chinese Journal of Engineering, 46(2), 209–217.
[13] Yao, Q. C., & Yu, J. L. (2021). Embedding and transformation: The logic of digital technology reshaping governance system and its grass-roots dilemma. Journal of Gansu Administration Institute, (5), 41–52+126.
[14] Yang, B. W. (2024). The gradual logic, transformation deployment and institutional orientation of China's implementation of carbon peaking and carbon neutrality. Contemporary Economic Research, (7), 105–116.
[15] Kaack, L. H., Donti, P. L., Strubell, E., et al. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12(6), 518–527. https://doi.org/ 10.1038/ s41 558-022-01389-8.
[16] Zhang, G. M., Wang, T., Lou, Y. Y., Guan, Z. C., Zheng, H. J., Li, Q., & Wu, J. Q. (2025). Research on China's Provincial Carbon Emission Peak Path Based on a LSTM Neural Network Approach. Chinese Journal of Management Science, 33(3), 339–350.
[17] Wang, X. L., Yang, A. N., Li, X., & Liu, F. (2026). How Environmental Data Element Sharing Can Promote Regularized Inter-Governmental Collaborative Governance——Empirical Evidence from Regional Synergistic Emission and Carbon Reduction. Journal of Public Management, 23(1), 153–165+176.
[18] Jing, C., Huang, C., & Su, J. (2024). Digital Technology Empowerment and Collaborative Governance Networks: An Analysis Based on Environmental Governance Practices in J City. Public Administration Review, 17(4), 55–72+196-197.
[19] Wang, S. T., & Yan, C. H. (2023). Modernization of Rural Environmental Governance Empowered by Digital Technology. Modern Economic Research, (12), 126–132.
[20] Hao, W. Q., Meng, X., & Duan, Z. H. (2023). Theoretical Logic and Configuration Path of Urban Digital Transformation from the Perspective of Dynamic Capabilities—A FsQCA Analysis Based on National Key Cities. E-Government, (7), 73–86.
[21] Chen, X. H., Hu, D. B., Cao, W. Z., Liang, W., Xu, X. S., Tang, X. B., & Wang, Y. J. (2021). Path of Digital Technology Promoting Realization of Carbon Neutrality Goal in China's Energy Industry. Bulletin of Chinese Academy of Sciences, 36(9), 1019–1029.
[22] Wen, H., & Wang, S. (2024). Towards Agile Governance of Mega-Cities: From the Perspective of Full-Domain Digital Transformation. E-Government, (12), 2–14.
[23] Peng, X. X. (2022). Big Data Promoting Intelligent Environmental Governance: Generation Logic, Practical Dilemmas and Innovative Paths. Xinjiang Social Sciences, (5), 157–167.
[24] Men, L. X., Zhang, Y. Y., Zhang, H. P., et al. (2023). Research on Benefit Distribution System of Public Data Authorized Operation. E-Government, (11), 14–27.
[25] Wang, D., & Zhang, Y. (2024). Corporate Digital Transformation, Strategic Green Innovation and Corporate Environmental Performance. Economic Research Journal, 59(10), 113–131.
[26] Liu, X. G. (2023). The Puzzle of “Instrumental Rationalism” in National Governance and Its Answer. Social Sciences in China, (6), 42–61+205.
[27] Zhang, H. C., & Yang, H. (2024). Embedding Ethics in Digital Government Construction: A Theoretical Analysis. Chinese Public Administration, 40(7), 133–142.
[28] Han, W. Q., & Wang, S. Y. (2023). The Turns and Prospects of Research on Automation Administration From External Governance to Internal Operation. Journal of Shanghai Administration Institute, 24(5), 48–57.
[29] Xiao, H. J. (2022). Algorithmic Responsibility: Theoretical Justification, Panoramic Portrait and Governance Paradigm. Management World, 38(4), 200–226.
[30] Xin, L., & Liu, T. Y. (2024). The Characteristics and Evolution of Algorithmic Risk Perception in Automated Administrative Discretion: Big Data Analysis Based on Online Public Opinions. Public Administration Review, 17(1), 45–65+197.
[31] Wu, J. J., & He, B. G. (2023). The Rise of Algorithmic Bureaucracy and Its Typology. Sociological Studies, 38(6), 40–60.
[32] Wu, X. L., & Xing, Y. F. (2023). Isomorphic Structure and Divided Responsibilities: The Vertical Government Responsibilities in the Building of Digital Government——A Case Study in Guangdong Province. Chinese Public Administration, 39(4), 14–21.
[33] Niu, L., & Xue, J. G. (2024). Bureaucracy Leveraging and Policy Bundling: Inter-Departmental Collaboration Logic in Government Data Sharing--A Case Study of District A Big Data Center. E-Government, (1), 10–21.
[34] Jia, K., Zhao, J., & Fu, H. Y. (2023). Dealing with Challenges of Uncertainty: Theoretical Definition of Agile Governance on Algorithms. Library and Information Science, 40(1), 35–44.
[35] Xie, X. Q., & Ren, S. H. (2022). Mega City governance driven by agile governance in the era of Digital Economy: Empirical evidence from the construction of smart city in Chengdu City. Urban Problems, (2), 86–95.
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